Alzheimer’s Clues in Blood, Brain & Beyond

Recent key publications

Machine learning in Alzheimer's disease genetics. 
Nat Commun 2025; 16:6726. Bracher-Smith M, Melograna F, Ulm B et al.

Link to full article: https://pubmed.ncbi.nlm.nih.gov/40691194/

Despite recent progress in the field, recurrent implantation failure (RIF) continues to pose a major obstacle in assisted reproductive technology (ART), with its complex underlying causes remaining poorly understood. While the 2023 ESHRE guidelines organised RIF classification by patient age, growing research indicates that other key factors, including anti-Müllerian hormone (AMH) levels and chronic endometritis, may also be crucial determinants.

This study uses machine learning to identify and prioritise risk factors associated with RIF. The results show that AMH is the most significant predictor, followed by endometrial disorders and BMI. These results provide new insights into the diagnosis and treatment of RIF, addressing important knowledge gaps in existing research and providing practical information to improve ART success rates. The findings emphasise the importance of adopting a comprehensive, multi-factor approach to evaluating RIF.

Circulating short chain fatty acids in Alzheimer's disease: A cross-sectional observational study. 
J Alzheimers Dis 2025; 106:38-43. Marizzoni M, Coppola L, Festari C et al.

Link to full article: https://pubmed.ncbi.nlm.nih.gov/40501283/

Recent investigations into the gut-brain axis have revealed intriguing links to the development of Alzheimer's disease, with a particular focus on the role of circulating short-chain fatty acids in this relationship. 

A new cross-sectional analysis examined plasma SCFA patterns in people with amyloid-positive Alzheimer's disease and revealed a characteristic signature of increased acetate and valerate levels alongside decreased butyrate concentrations. The most striking finding was the remarkable diagnostic performance of acetate, which achieved an area under the curve of 0.95 in distinguishing Alzheimer's patients from individuals with other cognitive impairments. This surpassed the accuracy of well-established biomarkers such as GFAP, suggesting that acetate could transform diagnostic approaches.

These results highlight SCFAs as compelling biomarker candidates that offer both diagnostic value and mechanistic insights into how gut dysfunction contributes to neurodegeneration. The distinct metabolic fingerprint observed in Alzheimer's patients opens up new avenues for understanding disease progression and for developing targeted interventions that address the gut-brain connection in cognitive decline.

Unravelling the plasma proteome: Pioneering biomarkers for differential dementia diagnosis. 
Alzheimers Dement 2025; 21:e70162. Gezegen H, Alaylıoğlu M, Şahin E et al.

Link to full article: https://pubmed.ncbi.nlm.nih.gov/40613333/

Distinguishing between Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia can be very challenging from a clinical perspective. These conditions share similar symptoms and pathological features, and existing biomarkers often fail to provide clear answers. To address this issue, researchers examined 120 plasma proteins in patients with confirmed diagnoses using the NULISA proteomics platform.

The study found that plasma pTau217 showed remarkable accuracy in identifying Alzheimer's disease, with an area under the curve of 0.90. In addition to this established marker, the analysis revealed three further proteins — CXCL1, SNAP25 and TREM1 — that could distinguish between non-Alzheimer's dementias and other forms.

This work demonstrates how blood-based protein panels could transform dementia diagnosis. Instead of relying on costly brain scans or invasive procedures, clinicians may soon be able to use straightforward blood tests to distinguish between different types of dementia. The identified protein signatures also reveal unique disease mechanisms, which could guide the development of future therapies for each specific condition.

Association of microplastics in human cerebrospinal fluid with Alzheimer's disease-related changes. 
J Hazard Mater 2025 Aug; 15:494:138748. He P, Wang F, Xi G et al.

Link to full article: https://pubmed.ncbi.nlm.nih.gov/40435616/

While growing concerns about microplastic pollution have mainly focused on organs such as the liver and lungs, new research reveals that these tiny particles also reach the brain. For the first time, scientists discovered polyethylene and polyvinyl chloride fragments in human cerebrospinal fluid, linking their presence to Alzheimer's disease markers and memory impairment.
This study builds on the recent finding of microplastics embedded in brain tissue and amyloid plaques. Patients with higher levels of microplastics showed poorer cognitive test results and elevated biomarkers associated with neurodegeneration. The particles appear to compromise the function of the blood-brain barrier, potentially accelerating brain ageing.

These results raise serious questions about the impact of everyday plastic exposure on brain health. Common items such as food packaging, water bottles and synthetic clothing release microscopic particles that can apparently cross into protected brain regions. The link between environmental contamination and dementia risk requires urgent research attention, given the widespread presence of plastic in modern life.

Polygenic Hazard Score for Predicting Age-associated Risk of Alzheimer's Disease in European Populations: Development and Validation. 
medRxiv 2025 Jul 28:2025.07.28.25332293. Akdeniz BC, Bahrami S, Hagen E et al.

Link to full article: https://pubmed.ncbi.nlm.nih.gov/40766162/

Doctors are increasingly recognising that an early diagnosis of Alzheimer's disease offers the best chance of effective treatment. However, predicting who will develop the condition and when remains difficult. Researchers have developed EADB720, a genetic scoring system that examines 720 DNA variants in order to estimate the risk of an individual developing the disease and the timing of its onset.

The team used advanced statistical methods called elastic net–regularised Cox regression to train their model on data from large European population studies. When tested against existing prediction tools, EADB720 demonstrated significantly greater accuracy in predicting the onset of Alzheimer's symptoms.

Multiple independent datasets confirmed these improvements, suggesting that the score works reliably across different populations. This breakthrough could enable doctors to identify high-risk patients years before symptoms appear, allowing for earlier interventions when treatments may be most effective. The tool also has the potential to enhance the design of clinical trials by enabling researchers to select participants who are most likely to develop the disease within the timeframe of the study.

Prognostic Value of a Multivariate Gut Microbiome Model for Progression from Normal Cognition to Mild Cognitive Impairment Within 4 Years. 
Int J Mol Sci 2025 May 15; 26 (10):4735. Bauch A, Baur J, Honold I et al.

Link to full article: https://pubmed.ncbi.nlm.nih.gov/40429881/

Scientists have long suspected that gut bacteria influence brain health. However, it remained unclear whether microbes could predict cognitive decline before symptoms appeared. In a recent study, researchers followed a group of healthy adults for four years, tracking both their gut microbiome composition and their mental function in order to identify early warning signs of mild cognitive impairment.

The study analysed bacterial DNA from stool samples, examining not only which species were present, but also the biological functions they performed. While counting bacterial types showed limited predictive power, mapping their metabolic activities proved to be a remarkably accurate predictor. A model based on these functional profiles correctly identified 84% of individuals who subsequently developed cognitive issues. This approach outperformed standard clinical assessments and simple bacterial counts.

The findings suggest that changes in gut microbial metabolism can occur years before noticeable memory loss. Such early detection could transform dementia prevention by enabling doctors to intervene before irreversible brain damage occurs. In the future, simple stool tests could replace expensive brain scans for screening high-risk patients.